AI-Driven Crop Monitoring and Management for Sustainable Agriculture

Pritha Ghosh (), Ajinkya Markad and Chandan Maharana
Additional contact information
Pritha Ghosh: Lovely Professional University
Ajinkya Markad: Lovely Professional University
Chandan Maharana: Murdoch University

Chapter Chapter 3 in Transforming Agriculture through Artificial Intelligence for Sustainable Food Systems, 2025, pp 39-53 from Springer

Abstract: Abstract Agriculture 5.0 seeks to create a sustainable food production system that balances affordability with ecosystem protection. Advancements in artificial intelligence (AI) are transforming agriculture by improving crop yield predictions and pest management. AI-based systems utilize diverse data sources and predictive models to assist farmers in strategic planning and informed decision-making. One notable innovation is AI-powered pest detection, which employs acoustic methods for pest classification and enables automated pesticide application. Farmers can remotely access these systems, reducing the need for field visits. Additionally, robotics and drones equipped with cameras and sensors enhance crop monitoring by providing real-time insights into crop health and identifying potential pest or disease threats. These technologies improve precision, save labor costs, and streamline crop management practices. In India, AI is becoming crucial for ensuring food security, enhancing yields, and minimizing economic losses. However, challenges remain, including the need for more accurate datasets, transparent models to build user trust, and the scalability of AI tools across various crops and farming practices. This chapter explores AI-based solutions for real-time crop monitoring and pest management, emphasizing their potential to revolutionize agriculture while addressing operational and scalability challenges.

Keywords: Robotic tools; Sustainability; Artificial intelligence; Monitoring; Crop protection (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-4795-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9789819647958

DOI: 10.1007/978-981-96-4795-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().